class_docstr: This class decodes each item in a batch by resizing each image
  window and save as a new image volume. Multiple output image can be
  proposed and csv output can be performed as well
class_docstr_tok: ["This","class","decodes","each","item","in","a","batch","by","resizing","each","image","window","and","save","as","a","new","image","volume",".","Multiple","output","image","can","be","proposed","and","csv","output","can","be","performed","as","well"]
docstr: Return the shape of the empty image to be saved
  :param image_id: index to find the appropriate input image from the
  reader
  :param n_channels: number of channels of the image
  :return:  shape of the empty image
docstr_tok: ["Return","the","shape","of","the","empty","image","to","be","saved",":","param","image_id",":","index","to","find","the","appropriate","input","image","from","the","reader",":","param","n_channels",":","number","of","channels","of","the","image",":","return",":","shape","of","the","empty","image"]
code: def _initialise_image_shape(self, image_id, n_channels):
      """
      Return the shape of the empty image to be saved
      :param image_id: index to find the appropriate input image from the
      reader
      :param n_channels: number of channels of the image
      :return:  shape of the empty image
      """
      self.image_id = image_id
      spatial_shape = self.input_image[self.name].shape[:3]
      output_image_shape = spatial_shape + (1, n_channels)
      empty_image = np.zeros(output_image_shape, dtype=np.bool)
      for layer in self.reader.preprocessors:
          if isinstance(layer, PadLayer):
              empty_image, _ = layer(empty_image)
      return empty_image.shape
  
file_path: NiftyNet/niftynet/engine/windows_aggregator_resize.py
imports: from __future__ import absolute_import, division, print_function
  import os
  from collections import OrderedDict
  import numpy
  import pandas
  import niftynet.io.misc_io
  from niftynet.engine.sampler_resize_v2 import zoom_3d
  from niftynet.engine.windows_aggregator_base import ImageWindowsAggregator
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.pad import PadLayer
label_desc: Use image node_id 3 for calling the ResizeSamplesAggregator obj's underlying member method code with example usage: obj._initialise_image_shape(image_id, n_channels) and returns: empty_image